Training and Capacity Building in LMIC for Research in Heart and Lung Diseases: The NHLBI—UnitedHealth Global Health Centers of Excellence Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Stemming the tide of noncommunicable diseases (NCDs) worldwide requires a multipronged approach. Although much attention has been paid to disease control measures, there is relatively little consideration of the importance of training the next generation of health-related researchers to play their important role in this global epidemic. The lack of support for early stage investigators in low- and middle-income countries interested in the global NCD field has resulted in inadequate funding opportunities for research, insufficient training in advanced research methodology and data analysis, lack of mentorship in manuscript and grant writing, and meager institutional support for developing, submitting, and administering research applications and awards. To address this unmet need, The National Heart, Lung, and Blood Institute-UnitedHealth Collaborating Centers of Excellence initiative created a Training Subcommittee that coordinated and developed an intensive, mentored health-related research experience for a number of early stage investigators from the 11 Centers of Excellence around the world. We describe the challenges faced by early stage investigators in low- and middle-income countries, the organization and scope of the Training Subcommittee, training activities, early outcomes of the early stage investigators (foreign and domestic) and training materials that have been developed by this program that are available to the public. By investing in the careers of individuals in a supportive global NCD network, we demonstrate the impact that an investment in training individuals from low- and middle-income countries can have on the preferred future of or current efforts to combat NCDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it